Carriers such as aircraft or even ships have many navigation systems. Examples of these systems especially are hybrid INS/GNSS equipment (Inertial Navigation System and Global Navigation Satellite System).
An inertial measurement unit (IMU) supplies low-noise information which is precise and short-term. However, in the long term, location performances of an inertial measurement unit degrade (more or less quickly as a function of the quality of sensors, accelerometers or gyroscopes for example, and processing used by the unit). If the information acquired from a radionavigation system by satellites are as such highly unlikely to drift in the long term, they are however often noisy and vary in precision. Also, inertial measurements are always available whereas GNSS information is not or is likely to be fooled and scrambled.
The hybridisation consists of combining information provided by the inertial measurement unit and measurements provided by the navigation system by satellites to produce information on position and speed by capitalising on both systems. Therefore, the precision of measurements provided by the GNSS receiver controls the inertial drift and low-noise inertial measurements filter out noise on measurements of the GNSS receiver.
The model of GNSS measurements which is known fails to consider any satellite failures which affect clocks or ephemerides transmitted, these failures generally manifesting in the form of bias or drifts added to the GNSS measurements.
In these terms, the aim of systems for integrity monitoring is detecting the appearance of these failures and excluding the responsible satellites to find a navigation solution containing no more undetected error.
According to the prior art, a bank of Kalman filters is conventionally used to protect against any failure of a satellite. In a INS/GNSS context, each filter performs hybridisation between information originating from the navigation system by satellite and that originating from the inertial measurement unit, and prepares a navigation solution. These filters use only some of the GNSS measurements available (typically all GNSS measurements with the exception of those originating from one of the satellites, the excluded satellite being different from one filter to the other).
FIG. 1 illustrates a navigation system 1 of the prior art adapted to carry out this principle.
The system 1 comprises a receiving module R adapted for acquisition of a primary set of measurements of signals emanating from radionavigation satellites, a data-processing unit D1, a bank B of Kalman filters F1-F6, and a decision-making unit P1.
The data-processing unit D1 provides each Kalman filter with a secondary set of respective measurements E11-E16, each secondary set comprising acquired measurements by exclusion of a specific measurement from the primary set.
If a satellite failure occurs, it is not viewed by the Kalman filter not receiving the measurement of the faulty satellite: this filter is therefore not affected by the failure and remains uncontaminated.
The decision-making unit P1 identifies the faulty satellite by comparing the measurements provided by the faulty satellite to estimations of these measurements prepared by each of the Kalman filters.
The satellite identified as being in failure can be excluded from the navigation solutions so as to cancel out pollution of the state of navigation by the satellite failure.
Now, multiplication of constellations of satellites dedicated to navigation (GPS, Galileo, Glonass for example) boosts the number of satellites which can be used in a hybrid INS/GNSS navigation system.
Therefore, future navigation systems requiring greater integrity will be restricted from having the capacity to detect and exclude more than one satellite failure.
In this aim, it is possible to adapt a bank of Kalman filters for detection and isolation of several simultaneous failures among N satellites.
For example, to detect two simultaneous satellite failures, one solution could consist of configuring each Kalman filter to process a respective subset of N−2 measurements of satellite signals, from which two measurements are excluded, each pair of excluded measurements being specific to a single Kalman filter.
Nevertheless, such a solution needs running a very large number of Kalman filters, that is, as many filters as couples of satellites selected from the N satellites in view, or
      C    N    2    =            N      ⁡              (                  N          -          1                )              2  filters. For example, for N=10, 45 filters are necessary; for N=20 there are 190 filters.